Microbial Identification Basics
One of the most important aspects of aseptic processing in pharmaceutical, cosmetic and food industries is the control of microbiological quality. Routine environmental monitoring and identification of microorganisms is required to recognize potential routes of contamination and demonstrate acceptable sterility levels throughout a given manufacturing process.
A variety of methods have been used over the past decade with varied levels of success to characterize recovered microorganisms.
Classical phenotypic methods require culture growth prior to analysis, and rely on visual characterization of the biochemical reaction catalyzed by an organism. The results are typically based on subjective judgement, as well as dependent on growth parameters and variability in the isolate’s cellular and colonial morphology, staining characteristics and phenotypic properties.
Cellular Fatty Acid Analysis provides microbial identification based on fatty acids that are extracted and methylated. The resultant methyl esters are separated by gas chromatography, and their patterns are compared against a characterization database.
Carbon Utilization is a method which tests an organism’s ability to utilize specific substrates, providing a metabolic fingerprint for comparison to an established database.
Genetic snapshot (RiboPrint® patterns) is an automated process which begins by lysing an organism’s cells and cutting the released DNA into fragments via a restriction enzyme. These fragments are separated by size through gel electrophoresis and then transferred to a membrane, where they are hybridized with a DNA probe and mixed with a chemiluminescent agent. A digitizing camera captures the light emission as image data and extracts a pattern which is compared to others in a database for characterization and identification.
DNA sequence technology begins the process of microbial identification with a DNA extraction from a pure isolate. All or part of the 16S rRNA gene is amplified, sequenced, and the resultant extension products are separated. The data is then matched in order of increasing genetic distance to relevant sequences in a database.
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